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构建和验证一个免疫浸润相关基因特征，用于预测乳腺癌的预后和治疗反应。

Construction and Validation of an Immune Infiltration-Related Gene Signature for the Prediction of Prognosis and Therapeutic Response in Breast Cancer.

机构信息

Department of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Department of Breast Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

出版信息

Front Immunol. 2021 Apr 27;12:666137. doi: 10.3389/fimmu.2021.666137. eCollection 2021.

DOI:10.3389/fimmu.2021.666137
PMID:33986754
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8110914/
Abstract

Breast cancer patients show significant heterogeneity in overall survival. Current assessment models are insufficient to accurately predict patient prognosis, and models for predicting treatment response are lacking. We evaluated the relationship between various immune cells and breast cancer and confirmed the association between immune infiltration and breast cancer progression. Different bioinformatics and statistical approaches were combined to construct a robust immune infiltration-related gene signature for predicting patient prognosis and responses to immunotherapy and chemotherapy. Our research found that a higher immune infiltration-related risk score (IRS) indicates that the patient has a worse prognosis and is not very sensitive to immunotherapy. In addition, a new nomogram was constructed based on the gene signature and clinicopathological features to improve the risk stratification and quantify the risk assessment of individual patients. Our study might contribute to the optimization of the risk stratification for survival and the personalized management of breast cancer.

摘要

乳腺癌患者的总生存存在显著异质性。目前的评估模型不足以准确预测患者的预后，也缺乏预测治疗反应的模型。我们评估了各种免疫细胞与乳腺癌之间的关系，并证实了免疫浸润与乳腺癌进展之间的关联。我们结合了不同的生物信息学和统计学方法，构建了一个稳健的免疫浸润相关基因特征，用于预测患者对免疫治疗和化疗的反应和预后。我们的研究发现，较高的免疫浸润相关风险评分（IRS）表明患者的预后较差，对免疫治疗的敏感性不高。此外，我们还基于基因特征和临床病理特征构建了一个新的列线图，以改善风险分层，并量化个体患者的风险评估。我们的研究可能有助于优化生存风险分层和乳腺癌的个体化管理。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11c5/8110914/45f85d39e5a6/fimmu-12-666137-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11c5/8110914/4e14ee4fe36f/fimmu-12-666137-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11c5/8110914/eced3d4a999c/fimmu-12-666137-g009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11c5/8110914/ee8a75820dcc/fimmu-12-666137-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11c5/8110914/e7a8a9e1e603/fimmu-12-666137-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11c5/8110914/eced3d4a999c/fimmu-12-666137-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11c5/8110914/45f85d39e5a6/fimmu-12-666137-g010.jpg

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